Effects of environmental colour on mood: a wearable life colour capture device

Colour is everywhere in our daily lives and impacts things like our mood, yet we rarely take notice of it. One method of capturing and analysing the predominant colours that we encounter is through visual lifelogging devices such as the SenseCam. However an issue related to these devices is the privacy concerns of capturing image level detail. Therefore in this work we demonstrate a hardware prototype wearable camera that captures only one pixel - of the dominant colour prevelant in front of the user, thus circumnavigating the privacy concerns raised in relation to lifelogging. To simulate whether the capture of dominant colour would be sufficient we report on a simulation carried out on 1.2 million SenseCam images captured by a group of 20 individuals. We compare the dominant colours that different groups of people are exposed to and show that useful inferences can be made from this data. We believe our prototype may be valuable in future experiments to capture colour correlated associated with an individual's mood

Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts

Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues. While there has been significant efforts on improving the quality of the algorithms, few works have tackled the harm that the artifacts generate in the predictions. In this work, we study fine tuning of pretrained networks to improve the resilience of previous methods to these artifacts. In our proposed method, we adopted the extensive usage of data augmentations that mimic those artifacts. The results significantly improved the baseline segmentations (up to 0.06 Dice score, and 4mm Hausdorff distance improvement).Comment: accepted for the STACOM2022 workshop @ MICCAI202

Cardiac Magnetic Resonance Phase Detection Using Neural Networks

The precision of cardiac magnetic resonance segmentation is an important area to investigate clinically and has received a lot of attention from the research community for its impact on the evaluation of cardiac functions. However, the correct identification of key time frames of cardiac sequences has received significantly less attention, especially in the MR domain, despite its great importance in the correct measurement of the Ejection Fraction, a key metric in diagnostics. In this paper, we present two deep learning regression methods to automate the otherwise time-consuming annotation process, with performance within the 1–2 frame distance error and almost instant calculation over short-axis images from a public dataset. Results are presented using publicly available data

Semi-supervised learning of cardiac MRI using image registration

In this work, we propose a method to aid the 2-D segmentation of short-axis cardiac MRI. In particular, the deformation fields obtained during the registration are used to propagate the labels to all time frames, resulting in a weakly supervised segmentation approach that benefits from the features in unlabelled volumes along with the annotated data. Experimental results over the M\&Ms datasets show that the addition of the synthetically obtained labels to the original dataset yields promising results in the performance and improves the capability of the network to generalise to scanners from different vendors

Cardiac MRI reconstruction from undersampled k-space using double-stream IFFT and a denoising GNA-UNET pipeline

In this work, we approach the problem of cardiac Magnetic Resonance Imaging (MRI) image reconstruction from undersampled k-space. This is an inherently ill-posed problem leading to a variety of noise and aliasing artifacts if not appropriately addressed. We propose a two-step double-stream processing pipeline that first reconstructs a noisy sample from the undersampled k-space (frequency domain) using the inverse Fourier transform. Second, in the spatial domain we train a denoising GNA-UNET (enhanced by Group Normalization and Attention layers) on the noisy aliased and fully sampled image data using the Mean Square Error loss function. We achieve competitive results on the leaderboard and show that the algorithmic combination proposed is effective in high-quality MRI reconstruction from undersampled cardiac long-axis and short-axis complex k-space data

Next generation PCR microfluidic system

Stokes Bio, founded in 2005, develops innovative microfluidic technologies. In 2008 in collaboration with Monsanto, an application driven development for a high-throughput instrument in the detection and characterisation of Single Nucleotide Polymorphisms (SNPs) in agricultural crops was initiated. Stokes technology is designed to generate aqueous nanolitre scale droplets of reagents and samples, wrapped in a carrier fluid from standard microtitre plates and to mix them using Stokes Bio’s proprietary liquid bridge mixers. Following mixing the complete assay is transferred in the carrier fluid through the use of a continuous flow system, to a flow through thermal cycler and an optical reading station. This poster summarises results collated using the Stokes Bio genotyping platform currently based in Monsanto. Data will be presented to illustrate the dynamic capabilities of the instrument, highlighting the enhanced sensitivity and reproducibility of PCR in droplet format compared to well-based technologies

Double-blind, 12 month follow-up, placebo-controlled trial of mifepristone on cognition in alcoholics: the MIFCOG trial protocol

Background: Increased levels of cortisol during acute alcohol withdrawal have been linked to cognitive deficits and depression. Preclinical research found that the glucocorticoid Type II receptor antagonist, mifepristone, prevented some of the neurotoxic effects of withdrawal and memory loss. Clinical trials have shown mifepristone effective in the treatment of depression. This study aims to examine the extent to which the glucocorticoid Type II receptor antagonist, mifepristone, when given to alcohol dependent males during the acute phase of alcohol withdrawal, will protect against the subsequent memory loss and depressive symptoms during abstinence from alcohol. Methods/Design: The study is a Phase 4 therapeutic use, “Proof of Concept” trial. The trial is a double-blind randomised controlled clinical trial of mifepristone versus inactive placebo. The trial aims to recruit 120 participants referred for an inpatient alcohol detoxification from community alcohol teams, who meet the inclusion criteria; 1) Male, 2) Aged 18–60 inclusive, 3) alcohol dependent for 5 or more years. A screening appointment will take place prior to admission to inpatient alcohol treatment units to ensure that the individual is suitable for inclusion in the trial in accordance with the inclusion and exclusion criteria. On admission participants are randomised to receive 600 mg a day of mifepristone (200 mg morning, afternoon and evening) for 7 days and 400 mg for the subsequent 7 days (200 mg morning and evening) or the equivalent number of placebo tablets for 14 days. Participants will remain in the trial for 4 weeks (at least 2 weeks as an inpatient) and will be followed up at 3, 6 and 12 months post randomisation. Primary outcome measures are cognitive function at week 3 and 4 after cessation of drinking and symptoms of depression over the 4 weeks after cession of drinking, measured using the Cambridge Neuropsychological Test Automated battery and Beck Depression Inventory, respectively. Secondary outcome measures are severity of the acute phase of alcohol withdrawal, alcohol craving, symptoms of protracted withdrawal and maintenance of abstinence and levels of relapse drinking at follow-up. Discussion: The current trial will provide evidence concerning the role of glucocorticoid Type II receptor activation in cognitive function and depression during acute alcohol withdrawal and the efficacy of treatment with mifepristone

Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees

Significance Contributions of rare variants to common and complex traits such as type 2 diabetes (T2D) are difficult to measure. This paper describes our results from deep whole-genome analysis of large Mexican-American pedigrees to understand the role of rare-sequence variations in T2D and related traits through enriched allele counts in pedigrees. Our study design was well-powered to detect association of rare variants if rare variants with large effects collectively accounted for large portions of risk variability, but our results did not identify such variants in this sample. We further quantified the contributions of common and rare variants in gene expression profiles and concluded that rare expression quantitative trait loci explain a substantive, but minor, portion of expression heritability.</jats:p

From cassava to gari: Mapping of quality characteristics and end-user preferences in Cameroon and Nigeria

User's preferences of cassava and cassava products along the value chain are supported by specific root quality characteristics that can be linked to root traits. Therefore, providing an evidence base of user preferred characteristics along the value chain, can help in the functional choice of cassava varieties. In this respect, the present paper presents the results from focus group discussions and individual interviews on user preferred quality characteristics of raw cassava roots and the derived product, gari, ‐ one of the major cassava products in Sub Saharan Africa ‐ in major production and consumption areas of Cameroon and Nigeria. Choice of cassava varieties for farming is mainly determined by the multiple end‐uses of the roots, their agricultural yield and the processing determinants of roots that support their major high‐quality characteristics: size, density, low water content, maturity, colour and safety. Processing of cassava roots into gari goes through different technological variants leading to a gari whose high‐quality characteristics are: dryness, colour, shiny/attractive appearance, uniform granules and taste. Eba, the major consumption form of gari in Cameroon and Nigeria is mainly characterized by its textural properties: smoothness, firmness, stickiness, elasticity, mouldability. Recommendations are made, suggesting that breeding will have to start evaluating cassava clones for brightness/shininess, as well as textural properties such as mouldability and elasticity of cassava food products, for the purpose of supporting decision‐making by breeders and the development of high‐throughput selection methods of cassava varieties. Women are identified as important beneficiaries of such initiatives giving their disadvantaged position and their prominent role in cassava processing and marketing of gari

Matrix Metalloproteinases in Cytotoxic Lymphocytes Impact on Tumour Infiltration and Immunomodulation

To efficiently combat solid tumours, endogenously or adoptively transferred cytotoxic T cells and natural killer (NK) cells, need to leave the vasculature, traverse the interstitium and ultimately infiltrate the tumour mass. During this locomotion and migration in the three dimensional environment many obstacles need to be overcome, one of which is the possible impediment of the extracellular matrix. The first and obvious one is the sub-endothelial basement membrane but the infiltrating cells will also meet other, both loose and tight, matrix structures that need to be overridden. Matrix metalloproteinases (MMPs) are believed to be one of the most important endoprotease families, with more than 25 members, which together have function on all known matrix components. This review summarizes what is known on synthesis, expression patterns and regulation of MMPs in cytotoxic lymphocytes and their possible role in the process of tumour infiltration. We also discuss different functions of MMPs as well as the possible use of other lymphocyte proteases for matrix degradation